# Bitwise train-rollout consistency

In reinforcement-learning post-training, the training engine and the rollout engine can compute different logprobs even with the same model, weights and inputs, because kernels, parallelism and reduction order differ, which affects the importance ratio, KL diagnostics and clipping. vime aligns the training timeline while RL-Kernel aligns numerical execution, and together they put attention, feed-forward, logprob and communication paths on one numerical contract on AMD Instinct MI300X. In a Qwen3-8B GRPO experiment on 8 MI300X GPUs, the strict path ran 200 consecutive steps with mismatch_count and max_abs_diff both zero.

In reinforcement-learning post-training, the training engine and the rollout engine can compute different logprobs even with the same model, weights and inputs, because kernels, parallelism and reduction order differ, which affects the importance ratio, KL diagnostics and clipping. vime aligns the training timeline while RL-Kernel aligns numerical execution, and together they put attention, feed-forward, logprob and communication paths on one numerical contract on AMD Instinct MI300X. In a Qwen3-8B GRPO experiment on 8 MI300X GPUs, the strict path ran 200 consecutive steps with mismatch_count and max_abs_diff both zero.